An example of an application for neuro-fuzzy inference apparatus is in the automation of health management (HM) in a complex system, in particular among a “system of systems” having network enabled capability (NEC). Monitoring such systems effectively is a difficult task, due to the large numbers of sensors, actuators and entities along with the uncertainty of the environment. A monitoring and diagnostic capability integrated with prognostics developments will enable the provision of: 1) real-time support of highly integrated NEC systems; 2) management of uncertainty/change in an NEC environment; 3) expert (human) knowledge for HM; and 4) real-time monitoring information associated with the health of system-of-systems (SoS) to assist human decision-making. Health management focuses on the reliable detection and monitoring of faults and failures of distributed assets. A degree of diagnostic capability already exists at the component level. The main challenge is to bring health management to different levels across a distributed system, as in NEC. thereby enabling major improvements in supportability and reconfiguration.
System diagnosis is an important part of HM systems. Diagnostics provide health information for use in prognostics, reconfiguration and decision-making functions. In recent years, a class of artificial intelligent (AI) technologies has been introduced to help engineers deal with large-scale complex network enabled systems in uncertain environments. Neuro-fuzzy inference (NFI) systems are possibly the best tools available for accounting for qualitative aspects of complexity such as the uncertainty of the environment and are well suited for decision making tasks. However, in such an NEC environment, the associated NFI operations in the fuzzification, the inference and the defuzzification stages increase the quantitative complexity of the problem; the quantitative complexity of problems increases the number of rules in the NFI system, when the number of inputs gets bigger.
In a parallel application having the same priority date and filing date as the present application, we disclose various measures for managing complexity in health monitoring applications for complex networked systems. In these systems, and in neuro-fuzzy or conventional fuzzy inference processors generally, there is a need to manage the low level computation of fuzzy inference in an efficient manner. In any fuzzy inference process, there is a need to evaluate membership functions rapidly according to which rules are fired and which are not. The logic and arithmetic steps involved at the lowest level become very onerous when the number of rules and variables increase, especially for applications in monitoring and control where real time decision making is important.
The invention has as its object to enable a reduction of the computational burden involved in computing fuzzy inferences.